Dear all,
I'm very pleased to announce the long-awaited release of the newest
version of pandas. It's the product of an absolutely huge amount of
development work primarily over the last 4 months. By the numbers:
- Over 550 commits over 6 months
- Codebase increased more than 60% in size
- More than 300 new test functions, with overall > 97% line coverage
The list of new features, improvements, and other changes is large,
but the main bullet points are are:
- Significantly enhanced GroupBy functionality
- Hierarchical indexing
- New pivoting and reshaping methods
- Improved PyTables/HDF5-based IO class
- Improved flat file (CSV, delimited text) parsing functions
- More advanced label-based indexing (getting/setting)
- Refactored former DataFrame/DataMatrix class into a single unified
DataFrame class
- Host of new methods and speed optimizations
- Memory-efficient "sparse" versions of data structures for mostly NA
or mostly constant (e.g. 0) data
- Better mixed dtype-handling and missing data support
For the full list of new features and enhancements since the 0.3.0
release, I refer interested people to the release notes on GitHub (see
link below).
In addition, the documentation (see below) has been nearly completely
rewritten and expanded to cover almost all of the features of the
library in great detail:
http://pandas.sourceforge.net
I expect more frequent releases of pandas going forward, especially
given the breadth and scope of the new functionality. I look forward
to user feedback (good and bad) on all the new functionality. Special
thanks to all the users who contributed bug reports, feature requests,
and ideas to this release.
best,
Wes
Links
=====
Release Notes: https://github.com/wesm/pandas/blob/master/RELEASE.rst
Documentation: http://pandas.sourceforge.net
Installers: http://pypi.python.org/pypi/pandas
Code Repository: http://github.com/wesm/pandas
Mailing List: http://groups.google.com/group/pystatsmodels
Blog: http://blog.wesmckinney.com
What is it
==========
**pandas** is a `Python <http://www.python.org>`__ package providing fast,
flexible, and expressive data structures designed to make working with
"relational" or "labeled" data both easy and intuitive. It aims to be the
fundamental high-level building block for doing practical, **real world** data
analysis in Python. Additionally, it has the broader goal of becoming **the
most powerful and flexible open source data analysis / manipulation tool
available in any language**. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
- Tabular data with heterogeneously-typed columns, as in an SQL table or
Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and
column labels
- Any other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, :class:`Series` (1-dimensional)
and :class:`DataFrame` (2-dimensional), handle the vast majority of typical use
cases in finance, statistics, social science, and many areas of
engineering. For R users, :class:`DataFrame` provides everything that R's
``data.frame`` provides and much more. pandas is built on top of `NumPy
<http://www.numpy.org>`__ and is intended to integrate well within a scientific
computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
- Easy handling of **missing data** (represented as NaN) in floating point as
well as non-floating point data
- Size mutability: columns can be **inserted and deleted** from DataFrame and
higher dimensional objects
- Automatic and explicit **data alignment**: objects can be explicitly
aligned to a set of labels, or the user can simply ignore the labels and
let `Series`, `DataFrame`, etc. automatically align the data for you in
computations
- Powerful, flexible **group by** functionality to perform
split-apply-combine operations on data sets, for both aggregating and
transforming data
- Make it **easy to convert** ragged, differently-indexed data in other
Python and NumPy data structures into DataFrame objects
- Intelligent label-based **slicing**, **fancy indexing**, and **subsetting**
of large data sets
- Intuitive **merging** and **joining** data sets
- Flexible **reshaping** and pivoting of data sets
- **Hierarchical** labeling of axes (possible to have multiple labels per
tick)
- Robust IO tools for loading data from **flat files** (CSV and delimited),
Excel files, databases, and saving / loading data from the ultrafast **HDF5
format**
- **Time series**-specific functionality: date range generation and frequency
conversion, moving window statistics, moving window linear regressions,
date shifting and lagging, etc.
Many of these principles are here to address the shortcomings frequently
experienced using other languages / scientific research environments. For data
scientists, working with data is typically divided into multiple stages:
munging and cleaning data, analyzing / modeling it, then organizing the results
of the analysis into a form suitable for plotting or tabular display. pandas
is the ideal tool for all of these tasks.